This research paper presents about the performance analysis of three phase modular multilevel converter with level shifted (phase disposition pulse width modulation) and phase shifted pulse width modulation (PWM) method. This research not only discusses the significance of different modulation procedures in the context of modular multilevel converters but also carefully evaluates each of their unique performance characteristics. This research is based on the fundamental creation of pulses and patterns that can only be acquired by comparing sinusoidal reference signals with triangular carrier signal. Both levels shifted (phase disposition) and phase shifted PWM procedures are used to calculate the phase output voltage of a converter at 2N+1 level. The output waveform is crucially shaped by these modulation techniques, which also ensure effective energy conversion and lower harmonic content. On the basis of the output voltage waveform's quality, the comparative research is conducted and simulated results were presented.

This research investigates on simulation of a traditional cascaded H-bridge (CHB) five-level inverter and proposes a nine-level cascaded H-bridge inverter system. The performance of both five-level and nine-level inverter systems is evaluated by modeling and simulating the open-loop system. According to the simulation results, the nine-level multilevel inverter (MLI) has a lower total harmonic distortion (THD) than the five-level MLI. The work also introduces a boost converter positioned between a photovoltaic power source and the inverter. A nine-level inverter system is utilized to simulate the proposed photovoltaic and battery-based buck-boost converter (BBC). The effectiveness of the proposed inverter is verified through simulation studies under various scenarios. In terms of THD, the comparison of the open-loop systems indicates that the nine-level inverter performs better than the five-level inverter. Additionally, simulations for a battery-based buck-boost converter and photovoltaic system used to verify the effectiveness of the proposed inverter.

This study examines the performance of a high-gain quadratic boost converter (QBC) coupled with a self-lift circuit under two control methodologies: sliding mode control (SMC) and fractional-order proportional integral derivative (FOPID) control. The QBC topology is used because it can boost voltage significantly, which is especially useful for renewable energy applications. Simulation studies show that both controllers can control the output voltage of the converter, but the FOPID controller works better in dynamic situations. In particular, it makes settling happen faster, cuts down on overshoot, and lowers steady-state error compared to the SMC method. The overall results show that the FOPID controller is a good choice for improving stability and transient response. This makes it a good choice for advanced high-performance power electronic systems.

This paper proposes a high-gain self-lift quadratic boost converter (SL-QBC) topology tailored for efficient marine propulsion drive systems. The presented converter architecture incorporates a self-lift circuit embedded within a quadratic boost stage to achieve significant voltage gain while maintaining low component stress and high conversion efficiency. Specifically, the converter is capable of boosting a low input voltage of 45 V to a high output voltage of 473 V. The high-voltage output is then fed to the marine propulsion system through a three-phase, three-level neutral point clamped (NPC) inverter, which drives a three-phase induction motor. Designed to interface low-voltage DC sources such as batteries, photovoltaic panels, or fuel cells with high-power marine propulsion motors, the proposed topology offers a compact and cost-effective solution for electric and hybrid marine applications. The self-lift mechanism enhances voltage boosting capability by leveraging additional inductive and capacitive energy transfer paths. The topology ensures continuous current operation, reduced voltage ripple, and improved dynamic response, all of which are critical for smooth and reliable marine drive performance. Simulation results validate the converter’s effectiveness, confirming its potential as a robust and efficient power conditioning stage in next-generation marine energy systems.

The prediction of lithium-ion battery capacity degradation plays a vital role in ensuring safe and efficient operation in electric mobility and renewable energy applications. This paper evaluates standalone machine learning, deep learning, and hybrid models for battery capacity estimation. The evaluated ML models include random forest, gradient boosting, and extreme gradient boosting (XGBoost), while the DL model employs a multilayer perceptron. The hybrid framework combines DL based feature extraction with ensemble ML regression or classification. A real-world dataset comprising temperature, resistance, reactance, and battery type was preprocessed, scaled, and divided into training and testing subsets. Hyperparameter tuning, k-fold cross-validation, and uncertainty quantification were incorporated to improve reliability and reproducibility. Model performance was assessed using RMSE, MAE, and R² for regression and receiver operating characteristic–area under the curve (ROC-AUC) and F1-score for classification. ROC curves, calibration curves, metric-comparison charts, cycle-wise degradation plots, and residual analyses were used for evaluation. Results demonstrate that the hybrid model outperforms standalone approaches by reducing RMSE and improving calibration, reliability, uncertainty alignment, and interpretability. This also establishes its novelty over existing state of health (SOH) models and highlights future extensions involving LSTM-based temporal modeling and chemistry-adaptive transfer learning. Overall, hybrid modeling provides a promising solution for reliable predictive battery maintenance.